What Is a Data Model?

Written by Coursera Staff • Updated on

A data model helps data analysts and database architects prepare data for analysis to gain insight into strategic company decisions. Learn the data model categories and their role in data analytics.

[Featured image] A data scientist works on a data model on a tablet.

A data model defines the relationships between data points, useful in software development, data migration, and system integration. Data models can be used to design new systems and software. In business intelligence, they can help database architects and data analysts gain insight into company data and its attributes, and set parameters for data grouping, sorting, storing, and formatting.

The purpose of a data model is to help communicate the computer system’s requirements, interactions with the data, and potential outcomes. Building a data model specific to the needs of each individual organization helps reduce errors, encourage consistency, save time, improve database performance, and develop communication between teams.

Placeholder

professional certificate

Google Data Analytics

Get on the fast track to a career in Data Analytics. In this certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required.

4.8

(151,362 ratings)

2,587,302 already enrolled

Beginner level

Average time: 6 month(s)

Learn at your own pace

Skills you'll build:

Data Analysis, Creating case studies, Data Visualization, Data Cleansing, Developing a portfolio, Data Collection, Spreadsheet, Metadata, SQL, Data Ethics, Data Aggregation, Data Calculations, R Markdown, R Programming, Rstudio, Tableau Software, Presentation, Data Integrity, Sample Size Determination, Decision-Making, Problem Solving, Questioning

Types of data models

Data models can be classified into three main categories based on the data’s abstraction levels (from lowest to highest abstraction):

1. Physical: A physical data model provides a specific blueprint of how the database will store data, helping technicians accurately create the design.

2. Logical: A logical data model omits technical specifications but includes essential details, such as data attributes, relationships, constraints, and subtypes. They can be applied to more than one database or system, meaning you could replicate the model for various products.

3. Conceptual: A conceptual data model offers a big-picture view of the system’s content and structure. It can also serve as a starting point for designing logical and physical models.

What is data modeling?

Data modeling helps analysts visualize data and set parameters to gain insights that enable making strategic business decisions. It also helps incorporate formulas, currencies, and data hierarchy for easier manipulation. Data models transform raw data into actionable insights.

Data model examples and techniques

You can use data models to help determine how you will store your data within a database. The database model you choose will depend partially on the needs of your business and the type of data, and it will provide a structure for how you store that data and how the data points relate to each other. For example, a common data model is a relational database, where your data is stored in tables and each row and column of data are related in a labelled way. You might use a relational database to store information about your contacts, such as first name, last name, phone number, email address, and social media handle. 

Another example of a data model is a hierarchical data model, where data is stored under broader parent categories. For example, a family tree is a hierarchical data model where each generation is visualized under the last in a wide pyramid shape. 

Placeholder

Related terms

Learn more about data analysis and data models on Coursera

Ready to learn more about data models to start a career in data analytics? Consider completing the Google Data Analytics Professional Certificate. This eight-course series is appropriate for beginners that want to learn skills like data analysis, data visualization, data ethics, and data collection.

Placeholder

professional certificate

Google Data Analytics

Get on the fast track to a career in Data Analytics. In this certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required.

4.8

(151,362 ratings)

2,587,302 already enrolled

Beginner level

Average time: 6 month(s)

Learn at your own pace

Skills you'll build:

Data Analysis, Creating case studies, Data Visualization, Data Cleansing, Developing a portfolio, Data Collection, Spreadsheet, Metadata, SQL, Data Ethics, Data Aggregation, Data Calculations, R Markdown, R Programming, Rstudio, Tableau Software, Presentation, Data Integrity, Sample Size Determination, Decision-Making, Problem Solving, Questioning

Updated on
Written by:
Coursera Staff

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Whether you're starting your career or trying to advance to the next level, experts at Google are here to help.

Save money and learn in-demand skills from top companies and organizations at your own pace.